Dr. Wolfgang Fuhl
University of Tübingen
Dpt. of Computer Science
Human-Computer Interaction
Sand 14
72076 Tübingen
Germany
- Telephone
- +49 - (0) 70 71 - 29 - 70492
- Telefax
- +49 - (0) 70 71 - 29 - 50 62
- wolfgang.fuhl@uni-tuebingen.de
- Office
- Sand 14, C206
- Office hours
- on appointment
Research Interest:
- Computer Vision (Classical image processing, Rule based algorithms, Shape estimation, 3D pose estimation, Detection, Classification, Segmentation, 3D reconstruction, Image generation, Modern Image, Features (HOG, MSER, SIFT), Real time systems)
- Machine Learning (Deep Neuronal Networks (Residual, Inception, Combinations, Recurrent), Tiny Convolutional Neuronal Networks, Real time Neuronal Networks (XOR, Binary, Tree), Unsupervised learning (Auto encoders, PCA), Support vector machines, Optimization, Decision Trees, KNN, GMM, transfer learning, Probabilistic (Naive Bays, HMM, CRF, Graph Models), Bagging, Boosting, Rule learning, PCA Networks, Scattering Networks, Clustering, Curve/Function fitting, evolutionary algorithms)
- Eye Tracking (Real time feature extraction algorithms, Feature based gaze estimation, Appearance based gaze estimation, AOI generation, Scan path classification, Eye movement detection, Usability of eye tracking software, Data visualization)
- Visualization (3D rendering, Data reduction, Interactive Visualizations, Splines)
- Hardware (FPGAs, raspberry PI, Mobile Phones, NPU, GPU, CPU)
Assigned-Thesis-Topics:
- Arduino Cloud for Mobile Phones, Benedikt Hosp (M.Sc.)
- Implementation And Evaluation Of Methods For Object Recognition, Sebastian Lutz (M.Sc.)
- EyeTrace - Saliency AOI Generation, Ying Meng (M.Sc.)
- Vergleich von Aufmerksamkeitsmodellen auf der Basis dynamischer Fahrszenen, Erik Lemke (B.Sc.)
- Extraktion der Blutgefäße des Auges aus Nahaufnahmen, Sotirios Pavlidis (B.Sc.)
- Bewertung Maschinelle Lernalgorithmen für die Güte von Programmierkenntnissen, Christian Hackenbeck (B.Sc.)
PDF - Bewertung und Umsetzung bewährter Echtzeitbildverarbeitungsmerkmale für den Einsatz in Webbrowsern, Amr Abdellatif (B.Sc.)
PDF - Bewertung und Umsetzung echtzeitfähiger maschineller Lern Algorithmen für den Einsatz in Webbrowsern, Hao liu (B.Sc.)
PDF - Erstellung und Bewertung eines Webbrowserbasierten Frameworks zur Datenakquise für Studien, Roufayda Salaheddine (B.Sc. in progress)
- Erstellung und Bewertung einer Webplattformbasierten Datenhaltungs- und Bewertungssoftware für Studien, Oliwia Oles (B.Sc. in progress)
- Improve Browser Watermarking with Eye Tracking, Nikolai Iraj Sanamrad (B.Sc.)
PDF - Scan path Classification in dynamic scenes, Fadi Al-kayid (B.Sc. in progress)
- Gaze based tessellation of objects in 3D scenes, Eric Goofers (M.Sc. in progress)
Publications
2020
RemoteEye: An open-source high-speed remote eye tracker
Benedikt Hosp, Shahram Eivazi, Maximilian Maurer, Woflgang Fuhl, David Geisler, and Enkelejda Kasneci. Behavior Research Methods, pages 1–15. Springer, 2020.
Training Decision Trees as Replacement for Convolution Layers
W. Fuhl, G. Kasneci, W. Rosenstiel, and E. Kasneci. Conference on Artificial Intelligence, AAAI, 2020.
Tiny convolution, decision tree, and binary neuronal networks for robust and real time pupil outline estimation
W. Fuhl, H. Gao, and E. Kasneci. ACM Symposium on Eye Tracking Research & Applications, ETRA 2020. ACM, 2020.
Neural networks for optical vector and eye ball parameter estimation
W. Fuhl, H. Gao, and E. Kasneci. ACM Symposium on Eye Tracking Research & Applications, ETRA 2020. ACM, 2020.
A Novel Camera-Free Eye Tracking Sensor for Augmented Reality based on Laser Scanning
Johannes Meyer, Thomas Schlebusch, Wolfgang Fuhl, and Enkelejda Kasneci. Sensors Journal, pages 1-1. IEEE, 2020.
Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction
Wolfgang Fuhl, Yao Rong, and Kasneci Enkelejda. Proceedings of the International Conference on Pattern Recognition, pages 0–0, 2020.
Explainable Online Validation of Machine Learning Models for Practical Applications
Wolfgang Fuhl, Yao Rong, Thomas Motz, Michael Scheidt, Andreas Hartel, Andreas Koch, and Enkelejda Kasneci. Proceedings of the International Conference on Pattern Recognition, pages 0–0, 2020.
Multi Layer Neural Networks as Replacement for Pooling Operations
Wolfgang Fuhl and Enkelejda Kasneci. arXiv preprint arXiv:2006.06969. CoRR, 2020.
Reinforcement learning for the privacy preservation and manipulation of eye tracking data
Wolfgang Fuhl, Efe Bozkir, and Enkelejda Kasneci. arXiv preprint arXiv:2002.06806. CoRR, 2020.
Differential Privacy for Eye Tracking with Temporal Correlations
Efe Bozkir, Onur Günlü, Wolfgang Fuhl, Rafael F. Schaefer, and Enkelejda Kasneci. arXiv preprint arXiv:2002.08972. CoRR, 2020.
Weight and Gradient Centralization in Deep Neural Networks
Wolfgang Fuhl and Enkelejda Kasneci. arXiv. CoRR, 2020.
Rotated Ring, Radial and Depth Wise Separable Radial Convolutions
Wolfgang Fuhl and Enkelejda Kasneci. arXiv. CoRR, 2020.
2019
Encodji: Encoding Gaze Data Into Emoji Space for an Amusing Scanpath Classification Approach ;)
Wolfgang Fuhl, Efe Bozkir, Benedikt Hosp, Nora Castner, David Geisler, Thiago C., and Enkelejda Kasneci. Eye Tracking Research and Applications, 2019.
Ferns for area of interest free scanpath classification
W. Fuhl, N. Castner, T. C. Kübler, A. Lotz, W. Rosenstiel, and E. Kasneci. Proceedings of the 2019 ACM Symposium on Eye Tracking Research & Applications (ETRA) , 2019.
Image-based extraction of eye features for robust eye tracking
W. Fuhl. PhD thesis. University of Tübingen, 2019.
500,000 images closer to eyelid and pupil segmentation
W. Fuhl, W. Rosenstiel, and E. Kasneci. Computer Analysis of Images and Patterns, CAIP, 2019.
The applicability of Cycle GANs for pupil and eyelid segmentation, data generation and image refinement
W. Fuhl, D. Geisler, W. Rosenstiel, and E. Kasneci. International Conference on Computer Vision Workshops, ICCVW, 2019.
Learning to validate the quality of detected landmarks
W. Fuhl and E. Kasneci. International Conference on Machine Vision, ICMV, 2019.
2018
PuRe: Robust Pupil Detection for Real-Time Pervasive Eye Tracking
T. Santini, W. Fuhl, and E. Kasneci. Elsevier Computer Vision and Image Understanding To Appear, 2018.
PuReST: Robust Pupil Tracking for Real-Time Pervasive Eye Tracking
T. Santini, W. Fuhl, and E. Kasneci. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA), 2018.
CBF:Circular binary features for robust and real-time pupil center detection
W. Fuhl, D. Geisler, T. Santini, T. Appel, W. Rosenstiel, and E. Kasneci. ACM Symposium on Eye Tracking Research & Applications, 2018.
Automatic generation of saliency-based areas of interest
W. Fuhl, T. Kübler, T. Santini, and E. Kasneci. Symposium on Vision, Modeling and Visualization (VMV), 2018.
Region of interest generation algorithms for eye tracking data
W. Fuhl, T. C. Kübler, H. Brinkmann, R. Rosenberg, W. Rosenstiel, and E. Kasneci. Third Workshop on Eye Tracking and Visualization (ETVIS), in conjunction with ACM ETRA, 2018.
MAM: Transfer learning for fully automatic video annotation and specialized detector creation
W. Fuhl, N. Castner, L. Zhuang, M. Holzer, W. Rosenstiel, and E. Kasneci. International Conference on Computer Vision Workshops, ICCVW, 2018.
Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools
W. Fuhl and E. Kasneci. Poster at Egocentric Perception, Interaction and Computing, EPIC, 2018.
BORE: Boosted-oriented edge optimization for robust, real time remote pupil center detection
W. Fuhl, S. Eivazi, B. Hosp, A. Eivazi, W. Rosenstiel, and E. Kasneci. Eye Tracking Research and Applications, ETRA, 2018.
Rule based learning for eye movement type detection
W. Fuhl, N. Castner, and E. Kasneci. International Conference on Multimodal Interaction Workshops, ICMIW, 2018.
Histogram of oriented velocities for eye movement detection
W. Fuhl, N. Castner, and E. Kasneci. International Conference on Multimodal Interaction Workshops, ICMIW, 2018.
Eye movement simulation and detector creation to reduce laborious parameter adjustments
W. Fuhl, T. Santini, T. Kuebler, N. Castner, W. Rosenstiel, and E. Kasneci. arXiv preprint arXiv:1804.00970, 2018.
2017
Saliency Sandbox: Bottom-Up Saliency Framework
D. Geisler, W. Fuhl, T. Santini, and E. Kasneci. 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.
EyeRecToo: Open-Source Software for Real-Time Pervasive Head-Mounted Eye-Tracking
T. Santini, W. Fuhl, D. Geisler, and E. Kasneci. 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.
EyeLad: Remote Eye Tracking Image Labeling Tool
W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, and E. Kasneci. 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.
Fast and Robust Eyelid Outline and Aperture Detection in Real-World Scenarios
W. Fuhl, T. Santini, and E. Kasneci. IEEE Winter Conference on Applications of Computer Vision (WACV 2017), 2017.
Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection
W. Fuhl, T. C. Kübler, D. Hospach, O. Bringmann, W. Rosenstiel, and E. Kasneci. Journal of Eye Movement Research 10(3), 2017.
CalibMe: Fast and Unsupervised Eye Tracker Calibration for Gaze-Based Pervasive Human-Computer Interaction
T. Santini, W. Fuhl, and E. Kasneci. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017.
Towards Intelligent Surgical Microscopes: Surgeons Gaze and Instrument Tracking
Shahram Eivazi, Wolfgang Fuhl, and Enkelejda Kasneci. Proceedings of the 22st International Conference on Intelligent User Interfaces, IUI 2017. ACM, 2017.
Towards automatic skill evaluation in microsurgery
Shahram Eivazi, Michael Slupina, Wolfgang Fuhl, Hoorieh Afkari, Ahmad Hafez, and Enkelejda Kasneci. Proceedings of the 22st International Conference on Intelligent User Interfaces, IUI 2017. ACM, 2017.
PupilNet v2.0: Convolutional Neural Networks for Robust Pupil Detection
W. Fuhl, T. Santini, G. Kasneci, and E. Kasneci. CoRR, 2017.
Fast camera focus estimation for gaze-based focus control
W. Fuhl, T. Santini, and E. Kasneci. CoRR, 2017.
Optimal eye movement strategies: a comparison of neurosurgeons gaze patterns when using a surgical microscope
S. Eivazi, A. Hafez, W. Fuhl, H. Afkari, E. Kasneci, M. Lehecka, and R. Bednarik. Acta Neurochirurgica, 2017.
2016
EyeRec: An Open-source Data Acquisition Software for Head-mounted Eye-tracking
T. Santini, W. Fuhl, T. C. Kübler, and E. Kasneci. Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 3: VISAPP: 386–391, 2016.
ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments
W. Fuhl, T. Santini, T. C. Kübler, and E. Kasneci. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA), pages 123–130, 2016.
Bayesian Identification of Fixations, Saccades, and Smooth Pursuits
T. Santini, W. Fuhl, T. C. Kübler, and E. Kasneci. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA), pages 163–170, 2016.
Pupil detection for head-mounted eye tracking in the wild: An evaluation of the state of the art
Wolfgang Fuhl, Marc Tonsen, Andreas Bulling, and Enkelejda Kasneci. Machine Vision and Applications, pages 1-14, 2016.
Eyes Wide Open? Eyelid Location and Eye Aperture Estimation for Pervasive Eye Tracking in Real-World Scenarios
W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, W. Rosenstiel, and E. Kasneci. ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.
Novel methods for analysis and visualization of saccade trajectories
T. C. Kübler, W. Fuhl, R. Rosenberg, W. Rosenstiel, and E. Kasneci. 3. ECCV Workshop VISART 2016, 2016.
Non-Intrusive Practitioner Pupil Detection for Unmodified Microscope Oculars
W. Fuhl, T. Santini, C. Reichert, D. Claus, A. Herkommer, H. Bahmani, K. Rifai, S. Wahl, and E. Kasneci. Elsevier Computers in Biology and Medicine 79: 36-44, 2016.
Evaluation of State-of-the-Art Pupil Detection Algorithms on Remote Eye Images
W. Fuhl, D. Geisler, T. Santini, and E. Kasneci. ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.
Feature-based attentional influences on the accommodation response
H. Bahmani, W. Fuhl, E. Gutierrez, G. Kasneci, E. Kasneci, and S. Wahl. Vision Sciences Society Annual Meeting Abstract, 2016.
PupilNet: Convolutional Neural Networks for Robust Pupil Detection
W. Fuhl, T. Santini, G. Kasneci, and E. Kasneci. CoRR, 2016.
2015
Analysis of eye movements with Eyetrace
T. C. Kübler, K. Sippel, W. Fuhl, G. Schievelbein, J. Aufreiter, R. Rosenberg, W. Rosenstiel, and E. Kasneci. 574: 458-471. Biomedical Engineering Systems and Technologies. Communications in Computer and Information Science (CCIS). Springer International Publishing, 2015.
Eyetrace2014: Eyetracking Data Analysis Tool
K. Sippel, T. C. Kübler, W. Fuhl, G. Schievelbein, R. Rosenberg, and W. Rosenstiel. 8th International Conference on Health Informatics, Healthinf 2015, 2015.
Exploiting the potential of eye movements analysis in the driving context
E. Kasneci, T. C. Kübler, C. Braunagel, W. Fuhl, W. Stolzmann, and W. Rosenstiel. 15. Internationales Stuttgarter Symposium Automobil- und Motorentechnik. Springer Fachmedien Wiesbaden, 2015.
ExCuSe: Robust Pupil Detection in Real-World Scenarios
W. Fuhl, T. C. Kübler, K. Sippel, W. Rosenstiel, and E. Kasneci. 16th International Conference on Computer Analysis of Images and Patterns (CAIP 2015), 2015.
Arbitrarily shaped areas of interest based on gaze density gradient
W. Fuhl, T. C. Kübler, K. Sippel, W. Rosenstiel, and E. Kasneci. European Conference on Eye Movements, ECEM 2015, 2015.
Teaching
Research
500,000 images closer to eyelid and pupil segmentation
We propose a fully convolutional neural networkfor pupil and eyelid segmentation as well as eyelid landmark and pupil ellipsis regression. The network is jointly trained using the Log loss forsegmentation and L1 loss for landmark and ellipsis regression. The ap-plication of the proposed network is the offline processing and creationof datasets. Which can be used to train resource-saving and real-timemachine learning algorithms such as random forests. In addition, we willprovide the worlds largest eye images dataset with more than 500,000images.
The applicability of Cycle GANs for pupil and eyelid segmentation, datageneration and image refinement
We evaluated Generative Adversarial Networks(GAN) for eyelid and pupil area segmentation, data gener-ation, and image refinement. While the segmentation GANperforms the desired task, the others serve as supportiveNetworks. The trained data generation GAN does not re-quire simulated data to increase the dataset, it simply usesexisting data and creates subsets. The purpose of the re-finement GAN, in contrast, is to simplify manual annota-tion by removing noise and occlusion in an image withoutchanging the eye structure and pupil position. In addition100,000 pupil and eyelid segmentations are made publiclyavailable for images from the labeled pupils in the wild dataset.
Neural networks for optical vector and eye ball parameter estimation
In this work we evaluate neural networks, support vector machinesand decision trees for the regression of the center of the eyeballand the optical vector based on the pupil ellipse. In the evaluationwe analyze single ellipses as well as window-based approaches asinput. Comparisons are made regarding accuracy and runtime. Theevaluation gives an overview of the general expected accuracy withdifferent models and amounts of input ellipses. A simulator wasimplemented for the generation of the training and evaluation data.For a visual evaluation and to push the state of the art in opticalvector estimation, the best model was applied to real data. Thisreal data came from public data sets in which the ellipse is alreadyannotated by an algorithm. The optical vectors on real data and thegenerator are made publicly available.
Eye labeling tool
Ground truth data is an important prerequisite for the development and evaluation of many algorithms in the area of computer vision, especially when these are based on convolutional neural networks or other machine learning approaches that unfold their power mostly by supervised learning. This learning relies on ground truth data, which is laborious, tedious, and error prone for humans to generate. In this paper, we contribute a labeling tool (EyeLad) specifically designed for remote eye-tracking data to enable researchers to leverage machine learning based approaches in this field, which is of great interest for the automotive, medical, and human-computer interaction applications. The tool is multi platform and supports a variety of state-of-theart detection and tracking algorithms, including eye detection, pupil detection, and eyelid coarse positioning.
Eye Movements Identification
Approaches for segmentation and synthesis of eye-tracking data using different neural networks and machine learning approaches.
Eyetrace
Eyetrace is a tool for analysis of eye-tracking data. It has the approach to bunch a variety of different evaluation methods for a large share of eye trackers supporting scientific work and medical diagnosis. To allow EyeTrace to be compatible to different eye trackers, an additional tool called Eyetrace Butler is used. The Eyetrace Butler performs a data preprocessing and conversion for analysis with Eyetrace. It provides plugins for different eye trackers and converts their data into a format that can be imported and used by Eyetrace.
Intelligent Surgical Microscope
Head-mounted eye tracking offers remarkable opportunities for research and applications regarding pervasive health monitoring, mental state inference, and human computer interaction in dynamic scenarios. Although a plethora of software for the acquisition of eye-tracking data exists, they often exhibit critical issues when pervasive eye tracking is considered, e.g., closed source, costly eye tracker hardware dependencies, and requiring a human supervisor for calibration. In this paper, we introduce EyeRecToo, an open-source software for real-time pervasive head-mounted eye-tracking. Out of the box, EyeRecToo offers multiple real-time state-of-the-art pupil detection and gaze estimation methods, which can be easily replaced by user implemented algorithms if desired. A novel calibration method that allows users to calibrate the system without the assistance of a human supervisor is also integrated. Moreover, this software supports multiple head-mounted eye-tracking hardware, records eye and scene videos, and stores pupil and gaze information, which are also available as a real-time stream. Thus, EyeRecToo serves as a framework to quickly enable pervasive eye-tracking research and applications.
Robust Pupil Detection and Gaze Estimation
The reliable estimation of the pupil position in eye images is perhaps the most important prerequisite in gaze-based HMI applications. While there are many approaches that enable accurate pupil tracking under laboratory conditions, tracking the pupil in real-world images is highly challenging due to changes in illumination, reflections on glasses or on the eyeball, off-axis camera position, contact lenses, and many more.